{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-03T08:06:15.006053300Z",
     "start_time": "2025-09-03T08:06:14.979750800Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "data = pd.read_csv('../data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1100 entries, 0 to 1099\n",
      "Data columns (total 31 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Attrition                 1100 non-null   int64 \n",
      " 1   Age                       1100 non-null   int64 \n",
      " 2   BusinessTravel            1100 non-null   object\n",
      " 3   Department                1100 non-null   object\n",
      " 4   DistanceFromHome          1100 non-null   int64 \n",
      " 5   Education                 1100 non-null   int64 \n",
      " 6   EducationField            1100 non-null   object\n",
      " 7   EmployeeNumber            1100 non-null   int64 \n",
      " 8   EnvironmentSatisfaction   1100 non-null   int64 \n",
      " 9   Gender                    1100 non-null   object\n",
      " 10  JobInvolvement            1100 non-null   int64 \n",
      " 11  JobLevel                  1100 non-null   int64 \n",
      " 12  JobRole                   1100 non-null   object\n",
      " 13  JobSatisfaction           1100 non-null   int64 \n",
      " 14  MaritalStatus             1100 non-null   object\n",
      " 15  MonthlyIncome             1100 non-null   int64 \n",
      " 16  NumCompaniesWorked        1100 non-null   int64 \n",
      " 17  Over18                    1100 non-null   object\n",
      " 18  OverTime                  1100 non-null   object\n",
      " 19  PercentSalaryHike         1100 non-null   int64 \n",
      " 20  PerformanceRating         1100 non-null   int64 \n",
      " 21  RelationshipSatisfaction  1100 non-null   int64 \n",
      " 22  StandardHours             1100 non-null   int64 \n",
      " 23  StockOptionLevel          1100 non-null   int64 \n",
      " 24  TotalWorkingYears         1100 non-null   int64 \n",
      " 25  TrainingTimesLastYear     1100 non-null   int64 \n",
      " 26  WorkLifeBalance           1100 non-null   int64 \n",
      " 27  YearsAtCompany            1100 non-null   int64 \n",
      " 28  YearsInCurrentRole        1100 non-null   int64 \n",
      " 29  YearsSinceLastPromotion   1100 non-null   int64 \n",
      " 30  YearsWithCurrManager      1100 non-null   int64 \n",
      "dtypes: int64(23), object(8)\n",
      "memory usage: 266.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-03T08:06:20.554464300Z",
     "start_time": "2025-09-03T08:06:20.538313400Z"
    }
   },
   "id": "1d2eabe4480562c5",
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "         Attrition          Age  DistanceFromHome    Education  \\\ncount  1100.000000  1100.000000       1100.000000  1100.000000   \nmean      0.161818    36.999091          9.427273     2.922727   \nstd       0.368451     9.037230          8.196694     1.022242   \nmin       0.000000    18.000000          1.000000     1.000000   \n25%       0.000000    30.000000          2.000000     2.000000   \n50%       0.000000    36.000000          7.000000     3.000000   \n75%       0.000000    43.000000         15.000000     4.000000   \nmax       1.000000    60.000000         29.000000     5.000000   \n\n       EmployeeNumber  EnvironmentSatisfaction  JobInvolvement     JobLevel  \\\ncount     1100.000000              1100.000000     1100.000000  1100.000000   \nmean      1028.157273                 2.725455        2.730909     2.054545   \nstd        598.915204                 1.098053        0.706366     1.107805   \nmin          1.000000                 1.000000        1.000000     1.000000   \n25%        504.250000                 2.000000        2.000000     1.000000   \n50%       1026.500000                 3.000000        3.000000     2.000000   \n75%       1556.500000                 4.000000        3.000000     3.000000   \nmax       2065.000000                 4.000000        4.000000     5.000000   \n\n       JobSatisfaction  MonthlyIncome  ...  RelationshipSatisfaction  \\\ncount      1100.000000    1100.000000  ...               1100.000000   \nmean          2.732727    6483.620909  ...                  2.696364   \nstd           1.109731    4715.293419  ...                  1.095356   \nmin           1.000000    1009.000000  ...                  1.000000   \n25%           2.000000    2924.500000  ...                  2.000000   \n50%           3.000000    4857.000000  ...                  3.000000   \n75%           4.000000    8354.500000  ...                  4.000000   \nmax           4.000000   19999.000000  ...                  4.000000   \n\n       StandardHours  StockOptionLevel  TotalWorkingYears  \\\ncount         1100.0       1100.000000        1100.000000   \nmean            80.0          0.788182          11.221818   \nstd              0.0          0.843347           7.825548   \nmin             80.0          0.000000           0.000000   \n25%             80.0          0.000000           6.000000   \n50%             80.0          1.000000          10.000000   \n75%             80.0          1.000000          15.000000   \nmax             80.0          3.000000          40.000000   \n\n       TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\ncount            1100.000000      1100.000000     1100.000000   \nmean                2.807273         2.746364        7.011818   \nstd                 1.291514         0.701121        6.223093   \nmin                 0.000000         1.000000        0.000000   \n25%                 2.000000         2.000000        3.000000   \n50%                 3.000000         3.000000        5.000000   \n75%                 3.000000         3.000000        9.000000   \nmax                 6.000000         4.000000       37.000000   \n\n       YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \ncount         1100.000000              1100.000000           1100.000000  \nmean             4.207273                 2.226364              4.123636  \nstd              3.618115                 3.313830              3.597996  \nmin              0.000000                 0.000000              0.000000  \n25%              2.000000                 0.000000              2.000000  \n50%              3.000000                 1.000000              3.000000  \n75%              7.000000                 3.000000              7.000000  \nmax             18.000000                15.000000             17.000000  \n\n[8 rows x 23 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Attrition</th>\n      <th>Age</th>\n      <th>DistanceFromHome</th>\n      <th>Education</th>\n      <th>EmployeeNumber</th>\n      <th>EnvironmentSatisfaction</th>\n      <th>JobInvolvement</th>\n      <th>JobLevel</th>\n      <th>JobSatisfaction</th>\n      <th>MonthlyIncome</th>\n      <th>...</th>\n      <th>RelationshipSatisfaction</th>\n      <th>StandardHours</th>\n      <th>StockOptionLevel</th>\n      <th>TotalWorkingYears</th>\n      <th>TrainingTimesLastYear</th>\n      <th>WorkLifeBalance</th>\n      <th>YearsAtCompany</th>\n      <th>YearsInCurrentRole</th>\n      <th>YearsSinceLastPromotion</th>\n      <th>YearsWithCurrManager</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>...</td>\n      <td>1100.000000</td>\n      <td>1100.0</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n      <td>1100.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.161818</td>\n      <td>36.999091</td>\n      <td>9.427273</td>\n      <td>2.922727</td>\n      <td>1028.157273</td>\n      <td>2.725455</td>\n      <td>2.730909</td>\n      <td>2.054545</td>\n      <td>2.732727</td>\n      <td>6483.620909</td>\n      <td>...</td>\n      <td>2.696364</td>\n      <td>80.0</td>\n      <td>0.788182</td>\n      <td>11.221818</td>\n      <td>2.807273</td>\n      <td>2.746364</td>\n      <td>7.011818</td>\n      <td>4.207273</td>\n      <td>2.226364</td>\n      <td>4.123636</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.368451</td>\n      <td>9.037230</td>\n      <td>8.196694</td>\n      <td>1.022242</td>\n      <td>598.915204</td>\n      <td>1.098053</td>\n      <td>0.706366</td>\n      <td>1.107805</td>\n      <td>1.109731</td>\n      <td>4715.293419</td>\n      <td>...</td>\n      <td>1.095356</td>\n      <td>0.0</td>\n      <td>0.843347</td>\n      <td>7.825548</td>\n      <td>1.291514</td>\n      <td>0.701121</td>\n      <td>6.223093</td>\n      <td>3.618115</td>\n      <td>3.313830</td>\n      <td>3.597996</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>18.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1009.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>80.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>30.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>504.250000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>2924.500000</td>\n      <td>...</td>\n      <td>2.000000</td>\n      <td>80.0</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>36.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>1026.500000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>4857.000000</td>\n      <td>...</td>\n      <td>3.000000</td>\n      <td>80.0</td>\n      <td>1.000000</td>\n      <td>10.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>5.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>43.000000</td>\n      <td>15.000000</td>\n      <td>4.000000</td>\n      <td>1556.500000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>8354.500000</td>\n      <td>...</td>\n      <td>4.000000</td>\n      <td>80.0</td>\n      <td>1.000000</td>\n      <td>15.000000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>9.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>60.000000</td>\n      <td>29.000000</td>\n      <td>5.000000</td>\n      <td>2065.000000</td>\n      <td>4.000000</td>\n      <td>4.000000</td>\n      <td>5.000000</td>\n      <td>4.000000</td>\n      <td>19999.000000</td>\n      <td>...</td>\n      <td>4.000000</td>\n      <td>80.0</td>\n      <td>3.000000</td>\n      <td>40.000000</td>\n      <td>6.000000</td>\n      <td>4.000000</td>\n      <td>37.000000</td>\n      <td>18.000000</td>\n      <td>15.000000</td>\n      <td>17.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-03T08:06:53.298403100Z",
     "start_time": "2025-09-03T08:06:53.261152700Z"
    }
   },
   "id": "481768e8bfac3b0d",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 都是数值, 取值少的实际还是类别, 比如 Education  1,2,3,4,5 5个类别\n",
    "- 取值多的比如 Age年龄  连续数值 "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f35833b8aa11a19d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 二分类问题, 目标值是类别, 特征值是连续多值数值型, 两种方式 画图 KDE  单特征AUC"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e3b2aee11fcb32bb"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x26d46c97740>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.kdeplot(data = data[data['Attrition']==1], x='Age',label = 'Attr')\n",
    "sns.kdeplot(data = data[data['Attrition']==0], x='Age',label = 'not')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='best')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-03T08:10:01.763468700Z",
     "start_time": "2025-09-03T08:10:01.640604100Z"
    }
   },
   "id": "1555ddb0195596a0",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x176d5de08d0>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.kdeplot(data = data[data['Attrition']==1], x='MonthlyIncome',label = 'Attr')\n",
    "sns.kdeplot(data = data[data['Attrition']==0], x='MonthlyIncome',label = 'not')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='best')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:51:00.999456900Z",
     "start_time": "2024-01-29T11:51:00.740361Z"
    }
   },
   "id": "bd643f90406022bf",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.351735967242682"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(data['Attrition'],data['Age'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:52:03.693666100Z",
     "start_time": "2024-01-29T11:52:03.424322500Z"
    }
   },
   "id": "31060b311baee253",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.3448505934826586"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['MonthlyIncome'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:52:56.703261200Z",
     "start_time": "2024-01-29T11:52:56.696680200Z"
    }
   },
   "id": "e993d1a4756644b8",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.15514940651734138"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.5-roc_auc_score(data['Attrition'],data['MonthlyIncome'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:53:11.119059400Z",
     "start_time": "2024-01-29T11:53:11.112838400Z"
    }
   },
   "id": "3309ce0bc0ad2273",
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 类别型"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6d64f4a4743fd843"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Education\n1    0.214286\n2    0.145631\n3    0.167053\n4    0.156146\n5    0.055556\nName: Attrition, dtype: float64"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Education')['Attrition'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:54:31.964555200Z",
     "start_time": "2024-01-29T11:54:31.951652600Z"
    }
   },
   "id": "357dcf4cde17968c",
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.47068841551098006"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['Education'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:55:30.022366200Z",
     "start_time": "2024-01-29T11:55:29.999251400Z"
    }
   },
   "id": "141cb03fbebaf587",
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "JobInvolvement\n1    0.380952\n2    0.168498\n3    0.146747\n4    0.106796\nName: Attrition, dtype: float64"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('JobInvolvement')['Attrition'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:56:04.410411900Z",
     "start_time": "2024-01-29T11:56:04.393200900Z"
    }
   },
   "id": "a973d377849ac5ef",
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.42820322211118966"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['JobInvolvement'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:56:16.085841200Z",
     "start_time": "2024-01-29T11:56:16.071798700Z"
    }
   },
   "id": "307b7fd1de9e1079",
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 所有特征按照这个套路遍历一圈, 可以找出有区分度的特征, 0.5附近 0.48 0.52 直接删除了\n",
    "# 先跑一个baseline "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bb66eac6d8d7cee"
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 进一步提升效果\n",
    " - 类别的可以进行编码\n",
    "    - 用离职率来编码\n",
    "    - woe p_good/p_bad   P_没离职/P_离职  toad\n",
    " - 数值型 分箱/编码\n",
    "    - 卡方分箱 WOE变换\n",
    "    - 自己做分箱尝试 /决策树 \n",
    " - 调整了一个特征就可以跑一下模型和baseline做比较"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "749e1e3ebf5cc0e0"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.47068841551098006"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(data['Attrition'],data['Education'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T12:00:57.921019200Z",
     "start_time": "2024-01-29T12:00:57.903601Z"
    }
   },
   "id": "2a585ca19124858c",
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.029311584489019937"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.5-roc_auc_score(data['Attrition'],data['Education'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T12:01:19.042267800Z",
     "start_time": "2024-01-29T12:01:19.017528900Z"
    }
   },
   "id": "bd87d8786b1e3550",
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "0.5448737478368959"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_encoding_dict = data.groupby('Education')['Attrition'].mean().to_dict()\n",
    "roc_auc_score(data['Attrition'],data['Education'].replace(edu_encoding_dict))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T12:01:05.472588900Z",
     "start_time": "2024-01-29T12:01:05.454202600Z"
    }
   },
   "id": "79a6899a0013ebd6",
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "35    59\n29    55\n36    55\n34    53\n31    48\n30    48\n32    47\n40    47\n33    47\n38    39\n27    38\n37    37\n28    35\n42    34\n41    31\n26    31\n45    30\n39    30\n46    25\n43    25\n50    23\n44    22\n49    22\n25    20\n24    18\n55    17\n51    16\n47    16\n54    15\n56    13\n53    13\n48    13\n22    12\n52    12\n23    10\n19     8\n59     8\n58     7\n21     7\n20     6\n60     4\n18     2\n57     2\nName: Age, dtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-29T11:45:19.644778200Z",
     "start_time": "2024-01-29T11:45:19.634267300Z"
    }
   },
   "id": "4fd8f2196cdb34ee",
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 还想进一步提升\n",
    "    - 尝试造新的特征 总工龄/在几家公司任职\n",
    "    - SMOTE过采样\n",
    "    - 数据清洗 孤立森林"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b037a0e8da14318d"
  }
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